Summary/Abstract Type 2 diabetes spans a broad and complex phenotypic spectrum, but its metabolic changes and progression start early, even years prior to diagnosis. Interventions have considerable preventive promise in these early stages. The challenge, however, is identifying the earliest changes of disease development and what is driving those changes. Metabolomic data provide crucial information to understand early metabolic changes and different etiologies of type 2 diabetes, especially with the longitudinal multi-omics setup proposed in this. We have been studying complex chronic diseases among Mexican Americans in Starr County, Texas to identify genetic and other risk factors leading to their disproportionate burden of disease. The main goal of this project is to understand underlying biological processes and pathways involved in worsening glycemic profiles and the development of type 2 diabetes. This study will analyze metabolomic profiles of 600 individuals at six time points, 300 with prediabetes and 300 with normal glycemia, in conjunction with separately funded diabetes prevention and microbiome studies. Genomic data on nearly all samples includes whole exome sequencing and GWAS genotyping with whole genome imputation. By adding several thousand metabolites to our extensive prospective longitudinal phenotypic and microbiome resources, we will identify metabolites that are associated with disease progression and are differentially associated with subtypes of prediabetes. We also have significant advantages to reveal causal relationship by longitudinal correlation analysis for lagged effects and by Mendelian randomization. Our pilot data already identified metabolomic signatures that are sensitive indicators of prediabetes status and its subtypes. We will perform integrative analyses of the proposed multi-omics dataset to understand pathways and their genomic underpinnings leading to prediabetes, diabetes, and progression with these specific aims: 1) Identify metabolites and pathways that are most indicative of worsening glycemia by analysis of longitudinal metabolomic profiles of individuals over the three years, 2) Identify metabolites and pathways that are most indicative of prevalent prediabetes status, 3) Identify metabolites that are highly associated with microbiota by analyzing temporal patterns of microbiomic and metabolomic profiles to understand their integrative role in the development and biology of prediabetes and diabetes, 4) Identify currently unnamed metabolites associated with prediabetes to construct a candidate set of unknown metabolites and verify the identity of unknown metabolites with computational analysis and experimental validation, and 5) (exploratory) Analyze genomic data to identify genes and variants associated with diabetes-related metabolites. With this project, we will bring together metabolomic, microbiomic and genomic profiles to identify changes in metabolic profiles of prediabetes in a longitudinal design. This will lead to understanding of the early progression of diabetes that will be exploitable for prevention and enhanced palliative approaches.